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Journal : Journal of Computer Science Artificial Intelligence and Communications

Sentiment Analysis of Social Media Towards Public Services Using Naive Bayes and Text Mining Rusmin Saragih; Mardiah; Deni Apriadi
Journal of Computer Science, Artificial Intelligence and Communications Vol 1 No 2 (2024): November 2024
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v1i2.18

Abstract

The rapid development of information and communication technology has driven the increased use of social media as a means of interaction between the public and service providers. Social media has become a platform for the public to express their opinions on the quality of services they receive, whether in the form of praise, suggestions, or complaints. Therefore, sentiment analysis of social media data can be a strategic tool in evaluating the performance of public services. This research aims to analyze public sentiment towards public services by utilizing text mining techniques and the Naive Bayes Classifier algorithm. The data used was collected from social media platforms such as Twitter and Facebook, followed by a text preprocessing stage that included tokenizing, stopword removal, and stemming. Subsequently, the data was analyzed to classify sentiment into positive, negative, and neutral categories. The test results show that the Naive Bayes algorithm is capable of classifying data with a satisfactory level of accuracy, making it an efficient method for monitoring public perception in real-time. This research contributes to supporting decision-making by government agencies regarding the improvement of public service quality based on publicly available feedback from social media
Evaluating the Impact of Knowledge Management Systems on Organizational Performance: A Technology Company Case Yasir, Amru; Apriadi, Deni; Siregar, Muhammad Noor Hasan; Handoko, Divi; Rahman, M. Arif
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 1 (2025): May 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i1.27

Abstract

This study aims to evaluate the impact of Knowledge Management Systems (KMS) on organizational performance within a technology company. In the digital era, knowledge has become a critical asset that drives innovation, efficiency, and competitive advantage. By leveraging a case study approach, the research examines how the implementation of KMS influences various performance indicators, including productivity, decision-making quality, employee collaboration, and knowledge retention. Data were collected through interviews, observations, and internal documents, and analyzed using a mixed-method approach. The findings suggest that effective use of KMS significantly improves organizational agility and innovation capabilities. However, the study also identifies challenges such as resistance to change, lack of user training, and insufficient integration with existing workflows. To maximize the benefits of KMS, organizations must foster a knowledge-sharing culture, provide ongoing support, and align KMS strategies with business objectives. The insights from this research are expected to contribute to the development of more effective knowledge management practices in technology-based organizations.